Inferring source polarisation properties from SKA observations. Joern Geisbuesch

Transcription

1 Inferring source polarisation properties from SKA observations In collaboration with Paul Alexander, Rosie Bolton, Martin Krause April 2008 University of Cambridge Department of Physics

2 Motivation and Overview: Explore the evolution and generation of magnetic fields throughout the Universe with up-coming and future instruments Especially study detectability of magnetic fields in galaxy clusters (largest gravitationally collapsed objects in the Universe with possibly very complicated magnetic field structures) with upcoming and future instruments (SKA and its pathfinders) 0) Synchrotron emission and Faraday rotation as magnetic field measure (RM) 1) Description of simulations (continuum and degree of polarisation) 2) RM Grid Statistics 3) Analysis of expected data (RM synthesis) 4) Results and Conclusions

3 Physical processes of importance: Polarised synchrotron emission: - Highly relativistic electrons gyrating in magnetic field (EM radiation emitted by accelerated charge; beaming due to relativistic velocities) - Synchrotron radiation spectrum of electron plasma : S ν ~ ν -α (low turn-over frequency, steepening at high frequencies) - Intrinsic degree of polarisation (electron plasma with a power law energy distribution): Emission up to ~70% polarised (viewing angle perpendicular to homogeneous B-field; for typical power law indices) Faraday effect: Effect of magneto-thermal electron plasma on transversing polarised radiation (scalar thermal electron density; line-of-sight B- field)

4 Simulating polarised sources Simulate continuum emission: Empirically: Assume luminosity functions for different source populations (i.e. AGN types, normal galaxies, starbursts), extrapolate and evolve; use linear theory to distribute (see e.g. Oxford team) Numerically: Run large (hydro-dynamical) N-body simulations, distribute sources in a halo and environment dependent fashion Model intrinsic degree of polarisation: Empirically: Polarisation model prob based on observational results (see e.g. Tucci et al. use a lognormal distribution at all frequencies; typically ~1%) Theoretically/numerically: Model (analytically or numerically) sources (electron densities and B-field distributions)

5 Modeling Faraday foreground screens (simple RM model) Cluster model and distribution: Cluster modeling based on mass function and linear density perturbations Scaling relations to obtain cluster properties (e.g. cluster specific radii) Electron density modeled by a standard β profile RM radial distribution based on results from local Universe Random uniform field amplitude matching local results Construct RM cluster image (still to include foregrounds and galaxies) RM grid building: Model source catalogue as described Model polarisation as described Model observations

6 Simulating Faraday screens

7 SKA Phase 1: RM Grid Statistics (cluster numbers and expected lines of sight through clusters) 1hr integration 100hrs integration T sys = 50 K, A/T = 1200 for 20 sqdeg m dishes Bandwidth: 400 MHz

8 RM Grid Statistics (cluster numbers and expected lines of sight through clusters) ASKAP: 1hr integration 100hrs integration T sys = 35 K, Area: 5089 sqm FoV: 30 sqdeg Bandwidth: 300 MHz

9 RM Grid Statistics (cluster numbers and expected lines of sight through clusters) MEERKAT: 1hr integration 100hrs integration T sys = 27 K, Area: sqm FoV: 2 sqdeg Bandwidth: 512 MHz

10 Faraday screens with turbulent fields I Electron gas density: (1+r 2 ) -3β/2, β=0.6 Magnetic field from random vector potential, modulated by (1+r 2 ) -3µ/2, µ=0.3, so average energy density ratio stays constant with radius Integrated rotation measure along line of sight Change of Faraday depth for different lines through a cluster of medium mass and for different values of the spectral index of the power spectrum (see also Murgia et al. 2004; Govoni et al for observations of fields in clusters)

11 n=2 Faraday screens with turbulent fields II Kolmogorov (n=11/3) Rotation measure structures for different values of the spectral index; normalised to have the same total magnetic field energy Power spectrum of multi-scale tangled divergence-free cluster magnetic field model: k 2 P(B)=k -(n-2) n=3 n=4

12 Depolarisation Along the line of sight: - scrambled/tangled magnetic fields (emission) - Differential Faraday rotation (source structure and geometry) By the telescope beam: -(Rapid) change of polarisation angle on the sky smoothed out by synthesised beam pattern and/or source illumination By the channel bandwidth: -Resolution of Faraday rotation feature (wavelength and bandwidth dependent) By temporal changes in the earth s atmosphere: -Ionospheric Faraday rotation (time varying turbulent screen)

13 Comparing field power spectra F depol in rest frame of cluster σ(rm)=200 rad/m 2 ~ 1 resolution for 0.6<z<5 Depolarisation frequency may provide an independent constraint on the power spectrum of the magnetic field Does need careful consideration for experiment design

14 MCMC RM synthesis Methodology: Use a Bayesian analysis in combination with a MCMC sampling method to explore the posterior. Assume source and screen models and apply model parameter priors (i.e. knowledge gained from past observations and simulations). Test for model preference by selection criterions to discriminate between different realistic/possible models. Bayes theorem: P(θ D,H) = [P(D θ,h) P(θ H)] / P(D H) MCMC: prescription for efficiently sampling from the parameter hypercube

15 Sampling the posterior Simulations: SKA AA observation of galaxy through screen; SKA AA specifications as in Schilizzi et al. Thinned and marginalised posterior sample distributions -> confidence intervals Prior assumptions: Model, redshift (e.g. SZ cluster detection followed up in optical)

16 MCMC RM synthesis results Uncertainties of foreground screen RM and radio galaxy intrinsic polarisation angle for single lines of sights Different colour shades represent different levels of confidence

17 Conclusions SKA pathfinders will be powerful instruments for studying B-field in clusters nearby and at moderate redshifts Full SKA is needed to go out to high redshifts Need to have prior knowledge on cluster positions and redshifts SZ and X-ray surveys and their follow-up will deliver this for clusters Developed a capable and efficient analysis tool Need to model full data processing pipeline and include Galactic and atmospheric screens Moreover need to include in our simulations Model cluster embedded sources and the interaction of extended sources with IGM (FR-I s and FR-II s) Further improve cluster field modeling and make predictions about evolution of sources (some predictions from cosmological MHD simulations; 12 redshift slices from z=5 to z=0)